8 papers
Improving End-to-End Models for Set Prediction in Spoken Language Understanding
Hong-Kwang J. Kuo, Zoltan Tuske, Samuel Thomas +2
The goal of spoken language understanding (SLU) systems is to determine the meaning of the input speech signal, unlike speech recognition which aims to produce verbatim transcripts…
4-bit Quantization of LSTM-based Speech Recognition Models
Andrea Fasoli, Chia-Yu Chen, Mauricio Serrano +9
We investigate the impact of aggressive low-precision representations of weights and activations in two families of large LSTM-based architectures for Automatic Speech Recognition…
Reducing Exposure Bias in Training Recurrent Neural Network Transducers
Xiaodong Cui, Brian Kingsbury, George Saon +2
When recurrent neural network transducers (RNNTs) are trained using the typical maximum likelihood criterion, the prediction network is trained only on ground truth label sequences…
Integrating Dialog History into End-to-End Spoken Language Understanding Systems
Jatin Ganhotra, Samuel Thomas, Hong-Kwang J. Kuo +4
End-to-end spoken language understanding (SLU) systems that process human-human or human-computer interactions are often context independent and process each turn of a conversation…
On the limit of English conversational speech recognition
Zoltán Tüske, George Saon, Brian Kingsbury
In our previous work we demonstrated that a single headed attention encoder-decoder model is able to reach state-of-the-art results in conversational speech recognition. In this pa…
RNN Transducer Models For Spoken Language Understanding
Samuel Thomas, Hong-Kwang J. Kuo, George Saon +5
We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding(SLU). These end-to-end (E2E) models are constructed in thr…